To meet the urgent need for high-precision tracking and reliable cataloging of non-cooperative targets in the Earth–Moon space, this paper proposes a GNSS Inter-Satellite Link and Connected Element Interferometry (CEI)-based measurement method for high-value cislunar space targets. Firstly, the general flow and basic scenario of the proposed method are given, followed by the mathematical model which, mainly includes four parts: (i) dynamical constraint equations for targets; (ii) GNSS-based interplanetary link for irradiation of targets; (iii) transmission loss equation of GNSS inter-satellite link signal in Earth–Moon space; (iv) CEI-based precision measurements of targets. On this basis, the full process link budget analysis is carried out, followed by the performance evaluation, which includes the reception performance of CEI receiving arrays and the measurement accuracy of targets. The feasibility of the proposed method is evaluated and verified in experiments, and it is illustrated that (i) for inter-satellite link visibility analysis, at least 20 satellites can simultaneously provide inter-satellite link signals to the Earth–Moon space targets, with a single GEO satellite up to $8.5$ h continuously, while the chain access can be available at up to 73,000 km, with the angle ranging from $-{80}^{\circ }$ to ${360}^{\circ }$; (ii) the Max Duration of Chain Access for BD3-lunarprobe-CEI (from 24 March 2023 04:00:00.000 to 31 March 2023 10:00:00.000) is 50,998.804 s/day, with a Total Duration of 358,408.797 s in 7 days; (iii) for link budget and measurement accuracy analysis, even beyond the farthest Earth–Moon Lagrangian point, the $C/N_0$ will be above $56.1$ dBHZ, while even approaching the distances of $4.5\times 10^5\mathrm{km}$, the ${\sigma }_{DLL}$ and ${\sigma }_{FLL}$ will be below 5.345 m and $3.475\times {10}^{-4}$ m/s, respectively, and the final measurement error will remain at 62.5 m with the proposed method. The findings of this paper could play a key role in future increasingly serious space missions, such as Earth–Moon space situational awareness, and will have a broad application prospect, if put into actual testing and operations.

As one of the most widespread and important types of terrestrial vegetation in the world, grasslands play an irreplaceable role in global climate change. The grasslands of Inner Mongolia, represented by the Xilin Gol League, are typical of Eurasian grasslands and have an important ecological status in the world. In this paper, taking the grassland of Xilin Gol League as the research object, based on the machine learning method, we mainly carry out two aspects of work: the prediction of grassland soil health and evaluation of grassland sustainable development. To address the issue of predicting soil health in grasslands, we focus on an important indicator in grasslands: soil moisture. By analyzing the characteristics of soil moisture time series values and related influencing factors, based on a NAR neural network model, three important factors of soil moisture were predicted: soil evaporation data, average air temperature, and precipitation. Subsequently, the corresponding soil moisture calculation model was trained using regression models based on hyperparameter optimization, and the final predicted soil moisture values were obtained for different months and depths in 2023 and 2024. To evaluate the sustainability of grassland development, we developed a model for the degree of grassland desertification based on the kernel principal component analysis, focusing on three dimensions: environmental factors, surface factors, and human factors. Based on this, a quantitative definition of soil denudation is given by analyzing the main influencing factors of grassland soil degradation. At the same time, a prediction model for the evaluation of soil slumping was established based on a fuzzy comprehensive evaluation matrix, and the evaluation weights of each major factor were given and analyzed. Based on the above research, this paper suggests a reasonable grazing strategy for the grassland areas of the Xilin Gol League: when the grazing intensity is medium and the total number of grazing days is [85, 104] days in a year, the degree of soil slumping and soil desertification in the pastures is minimized. The research results of this paper are useful for the future maintenance and management of the grasslands of Xilin Gol League and other similar areas.

With the development of low-orbit mega-constellations, low-orbit navigation augmentation systems, and other emerging LEO projects, the tracking accuracy requirement for low-orbit satellites is constantly increasing. However, existing methods have obvious shortcomings, and a new tracking and measurement method for LEO satellites is thus urgently needed. Given this, in this paper, a Connected Element Interferometry (CEI)-based “near-field” measurement model for low-orbit satellites is proposed. On this basis, the goniometric error formula of the model is derived, and the factors included in each error source are briefly discussed, followed by the simplification of the error formula. Furthermore, for the feasibility analysis of the proposed method, the common view time of CEI array on LEO satellites is analyzed in different regions and different baseline lengths. Finally, this paper simulates the effects of satellite–station distance, baseline length, and goniometric angle on the error coefficients in the goniometric error formula, and provides the theoretical goniometric accuracy of this model for different baseline lengths and goniometric angles. Under a baseline length of 240 km, the accuracy can reach 10 nrad. The research results of this paper could play the role of theoretical a priori in accuracy prediction in future low-orbit satellite tracking measurements.